Redefining the Agentic Engineer
The agentic engineering role is undergoing a fundamental transformation, yet many organizations remain trapped in an outdated paradigm. While the field is growing rapidly, with 2025 being hailed as the year AI agents enter the workforce, the role definition itself is stuck in the previous decade’s mindset.
Today’s conventional wisdom positions the agentic engineer as a developer who builds AI agents in code. Job postings emphasize skills like LangChain integration, LLM fine-tuning, and developing end-to-end generative AI solutions. This developer-centric view misses a crucial insight. The people best positioned to build reusable AI components are not necessarily the ones best equipped to translate business problems into agent-driven solutions.
The Emerging Reality
The rise of low-code and no-code AI agent platforms is democratizing agent development in ways that mirror the broader shift we’ve seen with tools like Cursor, Loveable and other vibe-coding environments. Product managers, business operations professionals, and customer success teams can now design, test, and deploy AI agents using visual tools and drag-and-drop interfaces- no deep coding required. This isn’t just hypothetical; platforms are already enabling non-technical users to launch working agents in days rather than waiting months for engineering sprints.
So who are the real agentic engineers of tomorrow? They won’t primarily be software developers. Instead, this role will be filled by implementation specialists who understand how to configure and orchestrate pre-built AI components to solve specific business workflows, much like solutions architects today. Implementation designers will map complex business requirements to agent capabilities, designing the interaction patterns and decision trees that make agents truly useful rather than merely technically impressive.
Perhaps most surprisingly, customer success managers are positioned to become exceptional agentic engineers. They intimately understand customer pain points and can rapidly prototype agent-based solutions, iterate based on real feedback, and continuously refine agent behavior to maximize value. These professionals bring something developers often lack: deep domain expertise, nuanced understanding of user needs, and the ability to think in terms of business outcomes rather than technical architecture.
Just as modern web developers increasingly compose applications from existing services and components rather than building everything from scratch, agentic engineers will work with ready-made “business blocks” - pre-built agent capabilities that solve real problems. Their value lies not in coding proficiency but in understanding which pre-built components solve which business problems, rapidly assembling and configuring agents to address specific use cases, and iterating quickly based on user feedback.
This evolution mirrors what happened with traditional software development. Low-code platforms didn’t eliminate developers, but they changed the nature of development work and opened it to new categories of builders. Similarly, agentic engineering is splitting into two distinct tracks: those who build the reusable infrastructure and models (traditional ML engineers and developers), and those who compose these components into business solutions (the new agentic engineers).
Organizations clinging to the developer-centric definition of agentic engineering risk two costly mistakes. First, they’re creating hiring bottlenecks by searching for unicorns who combine deep technical expertise with business acumen and product sense. Second, they’re missing the opportunity to empower their product, implementation, and success teams - people who already understand customer problems - to become agent builders.
The role of agentic engineer is not disappearing; it’s evolving. And the future belongs not to those who can build agents from scratch, but to those who can rapidly translate business needs into working agent-based solutions using the growing ecosystem of composable AI capabilities. The sooner organizations recognize this shift, the faster they can unlock the true potential of agentic AI.